VOLUME 18 NUMBER 1 (January to June 2025)

PSL%202021 vol14-no01-p12-28-Mikita%20and%20Padlan

SciEnggJ. 2025 18 (1) 201-209
available online: 08 June 2025
DOI: https://doi.org/10.54645/2025181QIS-68

*Corresponding author
Email Address: acjusi@up.edu.ph
Date received: 28 January 2025
Dates revised: 29 April 2025
Date accepted: 01 June 2025

ARTICLE

Terahertz spectroscopy and imaging of potential phantoms with defect classification

Keana Rylie E. Pasoquen1, Vincent Gene L. Otero1, Rovie S. de Ramos1, Mary Alyssa Benilde H. Aringay1, Prince Ranier C. Rana1, Gellin M. Gonzales1, Jonathan S. Morada1, Denzel Jeremiah B. Mulato1, Arvin Lester C. Jusi*1, Arnel A. Salvador2, Elmer S. Estacio2, and Alvin Karlo G. Tapia1

1Institute of Physics, University of the Philippines Los Baños,
      Laguna, 4031, Philippines
2National Institute of Physics, University of the Philippines Diliman,
      Quezon City, 1101, Philippines

KEYWORDS: terahertz spectroscopic imaging; imaging phantoms; principal component analysis

Terahertz (THz) spectroscopy and imaging were used for probing defects in potential imaging phantoms. Imaging phantoms are materials that can be used to mimic biological systems or general industrial materials. Polyvinyl acetate, silicone and plaster of Paris were used to simulate thin, gel-like, and solid systems, respectively. Defects and impurities were introduced for each material. The THz properties and images of the samples were determined using THz time-domain spectroscopy (THz-TDS). The THz absorption spectra and images were able to reveal variations between the control samples and samples with defects. This clearly shows that these variations are correlated with the defects introduced into the material. These results demonstrate the effectiveness of THz-TDS and imaging in detecting defects even across a wide variety of materials. Furthermore, using principal component analysis (PCA) of the THz images, it was observed that the THz properties varied depending on the type of defect introduced into the samples. PCA results showed distinct clusters for different defects and impurities in the samples. These results highlight a promising non-destructive method for identifying material defects, which can complement other non-destructive evaluation methods, ensuring the quality and safety of materials and products across various industries.

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